Data emitted over a period by a data source is also called a time-series. In statistics, signal processing, and many other fields, a time-series is a sequence of data points, measured typically at successive times, spaced according to uniform time intervals, other periodicity, or other triggers. An input time-series is a time-series that serves as input data. An output time-series is a time-series that is data produced from some processing. A time-series may be an output time-series of one object and an input time-series of another object.
Time-series analysis is a method of analyzing time-series, for example to understand the underlying context of the data points, such as where they came from or what generated them. As another example, time-series analysis may analyze a time-series to make forecasts or predictions. Time-series forecasting is the use of a forecasting model to forecast future events based on known past events, to wit, to forecast future data points before they are measured. An example in econometrics is the opening price of a share of stock based on the stock's past performance, which uses time-series forecasting analytics.
Time-series forecasting uses one or more forecasting models to regress a dependent factor on independent factors. For example, if an automotive dealer has been selling cars very quickly, the speed of sale is an example of an independent factor. A forecasting model regresses on historical data to predict the future sale rates. The future rate of sale is a dependent factor.